Search results for "python for generative ai"
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GenAI Security
This book is an essential guide for anyone looking to build secure and resilient generative AI applications. It provides actionable insights into identifying vulnerabilities and preventing attacks.Authored by security expert Paul Zenker, the book explores the critical aspects of securing GenAI systems across their lifecycle - design, development, and operation. Through numerous practical examples, detailed illustrations of threat scenarios, and application architectures, readers will gain a comprehensive understanding of potential risks. Step-by-step instructions cover techniques such as prompt injections, jailbreaks, and other attack vectors, equipping readers with the skills to anticipate and mitigate threats effectively.This resource is ideal for developers, security professionals, AI researchers, and organizations working with generative AI technologies. This book offers the tools and knowledge needed to create systems that can withstand sophisticated attacks in today’s high-stakes digital landscape.
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Python for Scientists
The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
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Information Access in the Era of Generative AI
Generative Artificial Intelligence (GenAI) has emerged as a groundbreaking technology that promises to revolutionize many industries as well as people’s personal and professional lives. This book discusses GenAI and its role in information access - often referred to as Generative Information Retrieval (GenIR) - or more broadly, information interaction.The role of GenAI in information access is complex and dynamic, with many dimensions. To address this, following a brief introduction to GenAI and GenIR, the remainder of the book provides eight chapters, each targeting a different dimension or sub-topic. These cover foundations of GenIR, interactions with GenIR systems, adapting them to users, tasks, and scenarios, improving them based on user feedback, GenIR evaluation, the sociotechnical implications of GenAI for information access, recommendations within GenIR, and the future of information access with GenIR.The book is targeted at graduate students and researchers interested in issues of information retrieval, access, and interactions, as well as applications of GenAI in various informational contexts. While some of the parts assume prior background in IR or AI, most others do not, making this book suitable for adoption in various classes as a primary source or as a supplementary material.
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AI Value Creators
We've arrived in a new era-GenAI is reshaping industries and decision-making processes across the board. As a result, understanding their potential and pitfalls has become crucial. But in order to stay ahead of the curve, you'll need to develop fresh perspectives on leveraging AI beyond mere technical know-how. Geared toward business leaders and tech professionals alike, this book demystifies the strategic integration of AI into business practices, ensuring you're equipped not just to participate but to lead in this new landscape.This insightful guide by industry leaders Rob Thomas, Paul Zikopoulos, and Kate Soule goes beyond the basics, offering real-life success stories and learned lessons to provide a blueprint for meaningful AI engagement. Whether you're a novice or a seasoned expert, you'll come away with an enhanced understanding of GenAI.Recognize the transformative potential of AI in business and how to harness itNavigate the ethical and operational challenges posed by AI with confidenceUnderstand the dynamic interplay between AI technology and business strategyImplement actionable strategies to integrate AI into your organizational cultureStep confidently into the role of an AI value creator, equipped to lead and innovate
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Generative AI and Education
This volume addresses the gap in knowledge around generative AI and its applications in education. It draws on the recent history of technological innovation and digital pedagogies, locating generative AI in the contemporary discourse around education futures. It argues that a new hybrid model of education is emerging, requiring educational institutions to embed generative AI into course and programme design, delivery and assessment. It also proposes a shift from a focus on learning as output to learning as a process, and explores what that shift might look like. Grounded in educational theory, it offers actionable pedagogy-informed guidance on how to position AI as a collaborator in the construction of learning in a manner that is congruent with the values and aims of education. It offers advice based on evidence-based digital pedagogy, including frameworks for effective teaching and learning with generative AI, that enable educators and designers to put these ideas into practice.The book also explores the wider context informing this shift, connecting narratives from governments and industry leaders on the strategic importance of generative AI and lifelong learning, learning agility, and future skills that make the case for the integration of competency-based assessment into mainstream education. Finally, going beyond pedagogy and practice, it considers of some of the broader issues, including limitations and ethics, and the implications of integrating generative AI into institutional planning and design, and includes a sample roadmap for education leaders to start the process of integrating generative AI into their institutions.This work approaches the topic of generative AI from the vantage point of educators by addressing themes and concerns that will resonate with professionals in the sector.
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Python for Mathematics
Python for Mathematics introduces readers to effective methods for doing mathematics using the Python programming language. Most programming texts introduce readers to the building blocks of programming and build up to using more sophisticated tools for a specific purpose, like doing particular mathematical tasks. This is akin to teaching someone how to forge metal so as to make a nail, and then slowly working up to using sophisticated power tools so as to actually build something. This book does things in a different way, by first getting readers to begin using and understanding the tools that are going to be helpful to them as mathematicians, and only then moving onto the granular details. In this way, the practical application of the tools can aid in the understanding of the theory.Features· Complete with engaging, practical exercises· Many useful and detailed coding examples· Suitable for undergraduates in mathematics, and other quantitative sciences· Empowers readers to design and create their own Python tools.
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Python Tools for Data Scientists
As part of the best-selling Pocket Primer series, this book is designed to provide an introduction to Python tools which are used by data scientists. It includes coverage of fundamental aspects of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. The first chapter contains a quick tour of basic Python, followed by a chapter introducing NumPy, and a chapter on Pandas. Chapter 4 provides a high-level view of Sklearn and SciPy. Chapter 5 contains an assortment of data cleaning tasks that are solved via Python and the awk programming language. Chapter 6 delves into data visualization with Matplotlib, Seaborn, and Bokeh. Next, one appendix explores issues that can arise with data, followed by an appendix on awk. Numerous code samples are used to illustrate concepts. Companion files with source code are available for downloading from the publisher. FEATURES:Introduces Python, NumPy, Sklearn, SciPy, and awk.Covers data cleaning tasks and data visualization.Features numerous code samples throughout.Includes companion files with source code .
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Hybrid Intelligence
Uniquely aimed at teams that think together to solve problems and make decisions, this book explains how to enhance the collective intelligence of a team-size group and combine it with the artificial intelligence (AI) of generative AI to create a hybrid intelligence that is smarter than either one on its own.Boards, committees, and other team-size groups of 5-20 people are the primary problem-solving and decision-making units within organizations, and they form the bridges between organizations, industries, and nations that collaborate on projects. So how can leaders exponentially improve their teams’ capabilities? Assemble the right people, arm them with the right processes, and execute those processes on the right platforms—then add generative AI to enhance those practices. This book describes and synthesizes various ways of increasing a group’s collective intelligence through people, process, and platform practices, and goes on to explain how to augment the practices with generative AI, including how to ask it questions and what questions to ask to obtain superior answers. The resulting upsurge in team capability enables organizations to survive and thrive in a world that is growing progressively more competitive and complex.This book will become the definitive resource for leaders and managers of commercial, government, and nonprofit organizations who want to learn how to significantly improve their teams’ problem-solving and decision-making ability by increasing their collective intelligence and combining it with the artificial intelligence of generative AI.
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